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Sci Rep. 2019 Dec 13;9(1):19036. doi: 10.1038/s41598-019-55395-1.

Encoding Temporal Regularities and Information Copying in Hippocampal Circuits.

Author information

1
Sussex Neuroscience, University of Sussex, Brighton, BN1 9QG, UK.
2
Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Brighton, BN1 9QJ, UK.
3
School of EECS, Queen Mary University of London, E1 4NS, London, UK.
4
Google DeepMind, London, N1C 4AG, UK.
5
Parmenides Center for the Conceptual Foundations of Science, 82049, Pullach, Munich, Germany.
6
Institute of Evolution, Centre for Ecological Research, 3 Klebelsberg Kuno Street, 8237, Tihany, Hungary.
7
Sussex Neuroscience, University of Sussex, Brighton, BN1 9QG, UK. philh@sussex.ac.uk.
8
Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Brighton, BN1 9QJ, UK. philh@sussex.ac.uk.
9
Sussex Neuroscience, University of Sussex, Brighton, BN1 9QG, UK. k.staras@sussex.ac.uk.

Abstract

Discriminating, extracting and encoding temporal regularities is a critical requirement in the brain, relevant to sensory-motor processing and learning. However, the cellular mechanisms responsible remain enigmatic; for example, whether such abilities require specific, elaborately organized neural networks or arise from more fundamental, inherent properties of neurons. Here, using multi-electrode array technology, and focusing on interval learning, we demonstrate that sparse reconstituted rat hippocampal neural circuits are intrinsically capable of encoding and storing sub-second-order time intervals for over an hour timescale, represented in changes in the spatial-temporal architecture of firing relationships among populations of neurons. This learning is accompanied by increases in mutual information and transfer entropy, formal measures related to information storage and flow. Moreover, temporal relationships derived from previously trained circuits can act as templates for copying intervals into untrained networks, suggesting the possibility of circuit-to-circuit information transfer. Our findings illustrate that dynamic encoding and stable copying of temporal relationships are fundamental properties of simple in vitro networks, with general significance for understanding elemental principles of information processing, storage and replication.

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